A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

The Interpreted Context Methodology (ICM) is a folder-based architecture for managing AI agent context. Instead of loading an entire codebase or writing monolithic prompt files, ICM organizes agent work into workspaces and stages — each with explicit declarations of what to load, what to skip, and exactly what the agent should do.

The core insight: AI agents perform better when they receive narrow, relevant context rather than broad, general context. ICM makes this narrowing systematic and repeatable.

• Открыть страницу с видео, видеоплеер должен быть виден на странице
• Открыть средства разработчика в браузере (F12), открыть вкладку с консолью
• Выполнить:
  document.querySelector('vk-video-player').store.actions.internal.downloadVideo()

Date: March 31, 2026 Scope: 16 apps from the casting/screen mirroring category on Google Play

The casting/screen mirroring category on Google Play is dominated by a small number of developer networks — primarily based in Vietnam and Pakistan — operating through multiple fake developer accounts, offshore shell companies, and systematic Play Store manipulation. These networks collectively account for ~1.8 billion installs across ~280+ apps.

By Stefan Hurzlmeier (@stefan_pledl) — March 31, 2026

I'm the developer of LocalCast, a casting app for Android. I've been competing in the "Cast to TV" / "Screen Mirroring" category on Google Play for years.

Recently, I noticed something: the ads showing up inside my own app were all for competing casting apps — and they all looked suspiciously similar. Same ad style, same kind of app, all targeting LocalCast users specifically. I collected 16 of these apps and started digging into who was behind them.

What I found was worse than I expected: a small number of developer networks — primarily based in Vietnam and Pakistan — operating 280+ apps across dozens of fake developer accounts, racking up a combined 1.8 billion installs through systematic Play Store manipulation. And they're all running coordinated ad campaigns targeting competing apps l

A simple example implementation of Retrieval Augmented Generation (RAG) using Google's Gemini API and ChromaDB for document storage and retrieval.

This code demonstrates a basic RAG system with three main components:

1. Document embedding using Google's text-embedding-004 model
2. Vector storage and retrieval using ChromaDB
3. Question answering using Gemini 1.5 Flash model